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| Content Provider | ACM Digital Library |
|---|---|
| Author | Imran, Muhammad Meier, Patrick Garcia Herranz, Manuel Lesa, Andre Castillo, Carlos |
| Abstract | In response to the growing HIV/AIDS and other health-related issues, UNICEF through their U-Report platform receives thousands of messages (SMS) every day to provide prevention strategies, health case advice, and counseling support to vulnerable population. Due to a rapid increase in U-Report usage (up to 300% in last 3 years), plus approximately 1,000 new registrations each day, the volume of messages has thus continued to increase, which made it impossible for the team at UNICEF to process them in a timely manner. In this paper, we present a platform designed to perform automatic classification of short messages (SMS) in real-time to help UNICEF categorize and prioritize health-related messages as they arrive. We employ a hybrid approach, which combines human and machine intelligence that seeks to resolve the information overload issue by introducing processing of large-scale data at high-speed while maintaining a high classification accuracy. The system has recently been tested in conjunction with UNICEF in Zambia to classify short messages received via the U-Report platform on various health related issues. The system is designed to enable UNICEF make sense of a large volume of short messages in a timely manner. In terms of evaluation, we report design choices, challenges, and performance of the system observed during the deployment to validate its effectiveness. |
| Starting Page | 61 |
| Ending Page | 65 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781450342247 |
| DOI | 10.1145/2896338.2896364 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-04-11 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Supervised machine learning Stream classification Hybrid system Text classification |
| Content Type | Text |
| Resource Type | Article |
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